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It would depend on whether we are doing a “Screening Design” or an “Optimization Design.” If the criticality of the 5 factors is yet not established, then we would go ahead with a Resolution V fractional factorial design. Out of the 5 factors, the factors with significant main effects can be further considered for a full factorial optimization design. Half Factorial - Screening Design: If the significance of the given 5 factors is questionable and are not yet validated as critical Xs, we can use a resolution V design to screen out non-critical factors. Half factorial experiments for screening are used primarily in two scenarios: 1) The existing historical data is inconclusive. 2) There is no historical data available at all. Impact on Time, Resources, and Complexity: A design summary of a fractional-factorial experiment with 5 factors and 2 levels with and without replication is shown below. Without replication With replication A fractional factorial design even with replication would require 32 runs which is almost half of a full factorial. Complexity also would be comparatively less as we would only be focusing primarily on the main effects and not the interactions. Since the effort is less too, fewer resources would be deployed. Full Factorial - Optimization Design – If all of these 5 factors are found to be critical, then we may want to optimize their behavior towards the response variable by conducting a full factorial experiment with replication. The following is the design summary for same: Impact on Time, Resources, and Complexity: A design summary of a full factorial experiment with 5 factors and 2 levels with replication is shown below. In comparison to a fractional factorial design, a full factorial experiment would be more time consuming as the number of runs would be more. We would have to conduct 64 runs with replication for a full factorial experiment. If any of these 5 factors warrant an addition of a centre point to rule out curvilinearity, then a few more runs would be added to it. Complexity would increase as interaction effects are also to be studied along with main effects and would necessitate utilization of more resources due to a sizeable number of experiments. We need to also factor in the Scope, Time, Cost and Resource constraints while conducting a full factorial experiment. Most of the R&D departments with higher risk appetite usually proceed with full factorial as they have to always come up with a robust design.